{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4046ee82",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>左侧砝码质量(kg)</th>\n",
       "      <th>理想底面积(mm²)</th>\n",
       "      <th>理想静压(MPa)</th>\n",
       "      <th>材料特性</th>\n",
       "      <th>有效面积(mm²)</th>\n",
       "      <th>实际静压(MPa)</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>1</td>\n",
       "      <td>铝</td>\n",
       "      <td>3.141464</td>\n",
       "      <td>0.999534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.140000</td>\n",
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       "      <td>铝</td>\n",
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       "      <td>2.998542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>4</td>\n",
       "      <td>铝</td>\n",
       "      <td>3.141558</td>\n",
       "      <td>3.998016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>5</td>\n",
       "      <td>铝</td>\n",
       "      <td>3.141589</td>\n",
       "      <td>4.997470</td>\n",
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       "      <th>...</th>\n",
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       "      <th>100</th>\n",
       "      <td>51.0</td>\n",
       "      <td>9.865095</td>\n",
       "      <td>51</td>\n",
       "      <td>碳化钨</td>\n",
       "      <td>9.897950</td>\n",
       "      <td>50.830714</td>\n",
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       "      <td>9.865095</td>\n",
       "      <td>53</td>\n",
       "      <td>碳化钨</td>\n",
       "      <td>9.897950</td>\n",
       "      <td>52.824074</td>\n",
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       "      <td>9.865095</td>\n",
       "      <td>54</td>\n",
       "      <td>碳化钨</td>\n",
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       "      <td>53.820754</td>\n",
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       "      <td>9.897950</td>\n",
       "      <td>54.817434</td>\n",
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      ],
      "text/plain": [
       "     左侧砝码质量(kg)  理想底面积(mm²)  理想静压(MPa) 材料特性  有效面积(mm²)  实际静压(MPa)\n",
       "0           1.0    3.140000          1    铝   3.141464   0.999534\n",
       "1           2.0    3.140000          2    铝   3.141495   1.999048\n",
       "2           NaN    3.140000          3    铝   3.141527   2.998542\n",
       "3           5.0    3.140000          4    铝   3.141558   3.998016\n",
       "4           1.0    3.140000          5    铝   3.141589   4.997470\n",
       "..          ...         ...        ...  ...        ...        ...\n",
       "100        51.0    9.865095         51  碳化钨   9.897950  50.830714\n",
       "101        52.0    9.865095         52  碳化钨   9.897950  51.827394\n",
       "102        53.0    9.865095         53  碳化钨   9.897950  52.824074\n",
       "103        54.0    9.865095         54  碳化钨   9.897950  53.820754\n",
       "104        55.0    9.865095         55  碳化钨   9.897950  54.817434\n",
       "\n",
       "[105 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# pd.set_option('display.precision', 15)  # 显示15位小数\n",
    "# # pd.set_option('display.float_format', '{:.15f}'.format)  # 强制显示15位\n",
    "data = pd.read_excel('./砝码实验有效数据集.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b17ef678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "左侧砝码质量(kg)     True\n",
       "理想底面积(mm²)    False\n",
       "理想静压(MPa)     False\n",
       "材料特性          False\n",
       "有效面积(mm²)     False\n",
       "实际静压(MPa)     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料特性编码\n",
    "material_mapping = {'铝': 1, '铁': 2, '碳化钨': 3}  # 添加其他材料映射\n",
    "data['材料特性'] = data['材料特性'].map(material_mapping)\n",
    "\n",
    "# 缺失值处理\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d294fdb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>理想静压(MPa)</th>\n",
       "      <th>材料特性</th>\n",
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       "      <th>实际静压(MPa)</th>\n",
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       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>9.897950</td>\n",
       "      <td>52.824074</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>54.0</td>\n",
       "      <td>9.865095</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>9.897950</td>\n",
       "      <td>53.820754</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
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       "      <td>55</td>\n",
       "      <td>3</td>\n",
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       "<p>104 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     左侧砝码质量(kg)  理想底面积(mm²)  理想静压(MPa)  材料特性  有效面积(mm²)  实际静压(MPa)\n",
       "0           1.0    3.140000          1     1   3.141464   0.999534\n",
       "1           2.0    3.140000          2     1   3.141495   1.999048\n",
       "3           5.0    3.140000          4     1   3.141558   3.998016\n",
       "4           1.0    3.140000          5     1   3.141589   4.997470\n",
       "5           5.0    3.140000          6     1   3.141621   5.996904\n",
       "..          ...         ...        ...   ...        ...        ...\n",
       "100        51.0    9.865095         51     3   9.897950  50.830714\n",
       "101        52.0    9.865095         52     3   9.897950  51.827394\n",
       "102        53.0    9.865095         53     3   9.897950  52.824074\n",
       "103        54.0    9.865095         54     3   9.897950  53.820754\n",
       "104        55.0    9.865095         55     3   9.897950  54.817434\n",
       "\n",
       "[104 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6adb348",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\LenovoSoftstore\\Install\\conda\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "D:\\LenovoSoftstore\\Install\\conda\\lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\86135\\AppData\\Roaming\\Python\\Python37\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
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\n",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据探索与可视化\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.pairplot(data[['左侧砝码质量(kg)', '理想底面积(mm²)', '理想静压(MPa)', '有效面积(mm²)', '实际静压(MPa)']])\n",
    "plt.suptitle('特征分布与关系', y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e49f83f",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'面积偏差百分比'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\LenovoSoftstore\\Install\\conda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2898\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2899\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '面积偏差百分比'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_4212\\1401471396.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# 面积偏差分布\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'面积偏差百分比'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'green'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'面积偏差百分比分布'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'百分比 (%)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\LenovoSoftstore\\Install\\conda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2904\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2905\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2906\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2907\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\LenovoSoftstore\\Install\\conda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2898\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2900\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2901\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2902\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '面积偏差百分比'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 材料分布\n",
    "plt.subplot(2, 2, 2)\n",
    "data['材料特性'].value_counts().plot(kind='bar', color=['skyblue', 'salmon'])\n",
    "plt.title('材料分布')\n",
    "plt.ylabel('数量')\n",
    "\n",
    "# 面积偏差分布\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.histplot(data['面积偏差百分比'], kde=True, bins=20, color='green')\n",
    "plt.title('面积偏差百分比分布')\n",
    "plt.xlabel('百分比 (%)')\n",
    "\n",
    "# 压力偏差分布\n",
    "plt.subplot(2, 2, 4)\n",
    "sns.scatterplot(x='理想静压(MPa)', y='压力偏差百分比', hue='材料特性', data=data, \n",
    "                palette={'1.0': 'blue', '2.0': 'green', '3.0': 'red'})\n",
    "plt.title('压力偏差百分比 vs 理想静压')\n",
    "plt.ylabel('偏差百分比 (%)')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('preprocessing_results.png', dpi=300)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
